Seismic exploration involves surveying subterranean geological formations for hydrocarbon deposits. A seismic survey may involve deploying seismic source(s) and seismic sensors at predetermined locations. The sources generate seismic waves, which propagate into the geological formations creating pressure changes and vibrations along their way. Changes in elastic properties of the geological formation scatter the seismic waves, changing their direction of propagation and other properties. Part of the energy emitted by the sources reaches the seismic sensors. Some seismic sensors are sensitive to pressure changes (e.g., hydrophones), others to particle motion (e.g., geophones), and industrial surveys may deploy one type of sensors or both. In response to the detected seismic events, the sensors generate electrical signals to produce seismic data. Analysis of the seismic data may be used to determine the presence or the absence of probable locations of hydrocarbon deposits.
With conventional techniques, some pre-stack and post-stack noise-reduction applications may use an estimate of the noise power spectrum, such as detection and replacement of noisy traces in the frequency spectrum due to, e.g., swell and barnacle noise, in the case of marine seismic data processing. Sometimes, Wiener filtering can be applied to noisy signals by utilizing random noise attenuation. Thus, random noise can be commonly estimated by averaging the data power spectrum of noisy traces by assuming that the noise is time-space stationary. In pre-stack seismic data, the noise can vary spatially and temporally. Also, robustness of noise power estimation, e.g., under low signal-to-noise ratio (SNR) conditions and non-stationary noise environments, can be affected by reliably tracking fast variations in the statistics of noisy traces. In multi-measurement towed streamer marine seismic data, when measuring full particle velocity vectors in addition to pressure, the noise spectrum is conventionally estimated in each component separately. However, this conventional technique is inefficient, and as such, other multi-measurement noise power spectrum approaches should be considered.